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def cmap_discretize(cmap, N): | |
"""Return a discrete colormap from the continuous colormap cmap. | |
cmap: colormap instance, eg. cm.jet. | |
N: number of colors. | |
""" | |
if type(cmap) == str: | |
cmap = get_cmap(cmap) | |
colors_i = np.concatenate((np.linspace(0, 1., N), (0.,0.,0.,0.))) | |
colors_rgba = cmap(colors_i) |
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#!/usr/bin/env python | |
import numpy as np | |
from astropy import units as u | |
from astropy import constants as const | |
@u.quantity_input # input validation decorator | |
def photon_ring_angular_size(mass: u.Msun, dist: u.pc): |
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def calc_time_to_next_comm(start=None, debug_prints=False): | |
''' | |
Queries the Kadi event database for the next several (scheduled) comm passes. | |
Calculates the duration between now (in UTC) and the next comm pass. | |
If that duration is negative (i.e. that comm already happened), it searches | |
the list of passes for the first positive time delta, i.e. the first comm pass in the future. | |
Returns a string in reporting the time to the next comm pass. | |
''' |
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import os | |
import shutil | |
import glob | |
filelist = glob.glob('*.png') | |
for image in filelist: | |
stringarray = image.split('_') | |
namearray = stringarray[-1].split('---')[-1].split('.')[0].split('-') |
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#!/usr/bin/env python3 | |
''' | |
A script for my cutie by her boy | |
''' | |
import numpy as np | |
import pandas as pd | |
import xlrd |
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import json | |
from matplotlib.colors import LinearSegmentedColormap | |
from matplotlib.cm import register_cmap | |
cdict_sunset = {'red': [[0.0, 0.10980392156862745, 0.10980392156862745], [0.09090909090909091, 0.14901960784313725, 0.14901960784313725], [0.18181818181818182, 0.17254901960784313, 0.17254901960784313], [0.2727272727272727, 0.22745098039215686, 0.22745098039215686], [0.36363636363636365, 0.2549019607843137, 0.2549019607843137], [0.45454545454545453, 0.2901960784313726, 0.2901960784313726], [0.5454545454545454, 0.38823529411764707, 0.38823529411764707], [0.6363636363636364, 0.5333333333333333, 0.5333333333333333], [0.7272727272727273, 0.7058823529411765, 0.7058823529411765], [0.8181818181818182, 0.8392156862745098, 0.8392156862745098], [0.9090909090909091, 0.9764705882352941, 0.9764705882352941], [1.0, 1.0, 1.0]], 'green': [[0.0, 0.09803921568627451, 0.09803921568627451], [0.09090909090909091, 0.1411764705882353, 0.1411764705882353], [0.18181818181818182, 0.16470588235294117, 0.16470588235294117], [0.272 |
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#!/usr/bin/env/python | |
import requests | |
from astropy.io import ascii | |
table = ascii.read('all_astro2020_whitepapers.csv') | |
numbers = table['Response ID'] | |
people = table['Last Name:Principal Author'] | |
urls = table['1:Upload File'] |
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#!/usr/bin/env python | |
import glob | |
import numpy as np | |
import matplotlib.pyplot as plt | |
from astropy.io import fits | |
from astropy.table import Table, vstack |
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def make_stars(kintable, fovimage, redshift, name, snthresh=100, velocity_thresh=None, project_by_median=True, project_by_redshift=False, cropbox=None, zoom=False, zoombox=None, wcs=True, nancolor='white', colorbar_scalefactor=0.047, vel_vmin=-500, vel_vmax=500, disp_vmin=0, disp_vmax=300, save=True, file_save_directory="./"): | |
table = fits.getdata(kintable) | |
#hdr = fits.getheader(fovimage) | |
x = table['x_cor'] | |
y = table['y_cor'] | |
# Get the 2D dimensions into which you'll paint this data | |
fovdata = fits.getdata(fovimage) |
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def fusemusealma(muse_moment_fits, alma_moment_fits): | |
''' | |
Open MUSE and ALMA HDUs, pick up their WCSs, | |
drop needless ALMA WCS axes, reproject | |
ALMA to MUSE, then output new HDUs. | |
''' | |
museHDU = fits.open(get_pkg_data_filename(muse_moment_fits)) | |
almaHDU = fits.open(get_pkg_data_filename(alma_moment_fits)) |
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